Recent advances in autonomous robotic technologies have highlighted the growing need for precise environmental analysis. LiDAR semantic segmentation has gained attention to accomplish fine-grained scene understanding by acting directly on raw content provided by sensors. Recent solutions showed how different learning techniques can be used to improve the performance of the model, without any architectural or dataset change. Following this trend, we present a coarse-to-fine setup that LEArns from classification mistaKes (LEAK) derived from a standard model. First, classes are clustered into macro groups according to mutual prediction errors; then, the learning process is regularized by: (1) aligning class-conditional prototypical feature representation for both fine and coarse classes, (2) weighting instances with a per-class fairness index. Our LEAK approach is very general and can be seamlessly applied on top of any segmentation architecture; indeed, experimental results showed that it enables state-of-the-art performances on different architectures, datasets and tasks, while ensuring more balanced class-wise results and faster convergence.

翻译：自主机器人技术的最新进展凸显了对精确环境分析的日益增长需求。通过直接处理传感器提供的原始内容，LiDAR语义分割因能实现细粒度场景理解而备受关注。近期研究表明，在不改变架构或数据集的情况下，可运用不同学习技术提升模型性能。基于这一趋势，我们提出一种从标准模型分类错误中学习（LEARN-LEAK）的由粗到细框架。首先，根据互预测错误将类别聚类为宏观分组；然后通过以下方式对学习过程进行正则化：（1）对齐细粒度与粗粒度类别的类条件原型特征表示，（2）通过基于类别的公平性指标对样本进行加权。我们的LEAK方法具有高度通用性，可无缝应用于任意分割架构之上。实验结果表明，该方法能在不同架构、数据集及任务中实现最优性能，同时确保更均衡的类别结果并加速收敛。